Online Learning for Large Scale Structured Data in Complex Situations

复杂情况下大规模结构化数据的在线学习

• 批准号: DP140102270
• 负责人: Prof Javen Qinfeng Shi
• 金额: $ 26.23万
• 依托单位: The University of Adelaide
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2014
• 资助国家: 澳大利亚
• 起止时间: 2014-01-01 至 2018-12-20
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP140102270
• 关键词: Online; Learning; Large; Scale; Structured

Online Learning (OL) is the process of predicting answers for a sequence of questions. OL has enjoyed much attention in recent years due to its natural ability of processing large scale non-structured data and adapting to a changing environment. However, OL has three weaknesses: it does not scale for structured data; it often assumes that all of the data are equally important; it often considers that all of the data are complete and noise-free. These weaknesses limit its utility, because real data such as those that must be analysed in processing social networks, fraud detection do not satisfy the restrictions. The aim of this project is to develop theoretical and practical advances in OL that overcome the existing weaknesses.

在线学习（OL）是预测一系列问题答案的过程。OL由于其处理大规模非结构化数据和适应环境变化的能力，近年来受到了广泛的关注。然而，OL有三个弱点：它不能扩展结构化数据;它经常假设所有数据都同样重要;它经常认为所有数据都是完整的，没有噪音。这些弱点限制了它的实用性，因为诸如在处理社交网络、欺诈检测时必须分析的那些真实的数据不满足这些限制。该项目的目的是在OL的理论和实践方面取得进展，克服现有的弱点。